Near field communication as sensor to cloud service interface

Leikanger, Tore; Schuss, Christian; Häkkinen, Juha

doi:10.1109/icsens.2017.8233998

Cited by 5 publications

(1 citation statement)

References 3 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data acquisition mostly depends on various sensors, such as sensors existing in user equipment, smart meters, and electricity stations. Leikanger et al [14] proposed a new method to upload sensor data directly to the online cloud through NFC, which solves the disadvantage that early data needs to be submitted to the mobile phone before forwarding. To improve the security of the electricity information acquisition systems and reduce the risk of attack damage, Li et al [15] proposes an electricity information acquisition system based on blockchain technology.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

Xie

Luo

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%